DocArrayInMemoryVectorStore 是 Docarray 提供的文档索引，将文档存储在内存中。对于小型数据集来说，这是一个很好的起点，因为你可能不想为此启动数据库服务器。

如果你在 Colab 中打开这个 Notebook，可能需要先安装 LlamaIndex 🦙。

%pip install llama-index-vector-stores-docarray

!pip install llama-index

import os
import sys
import logging
import textwrap

import warnings

warnings.filterwarnings("ignore")

# stop huggingface warnings
os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Uncomment to see debug logs
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import (
    GPTVectorStoreIndex,
    SimpleDirectoryReader,
    Document,
)
from llama_index.vector_stores.docarray import DocArrayInMemoryVectorStore
from IPython.display import Markdown, display

import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"

下载数据

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(
    "Document ID:",
    documents[0].doc_id,
    "Document Hash:",
    documents[0].doc_hash,
)

Document ID: 1c21062a-50a3-4133-a0b1-75f837a953e5 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e

初始化与索引

from llama_index.core import StorageContext

vector_store = DocArrayInMemoryVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = GPTVectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

查询

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(textwrap.fill(str(response), 100))

Token indices sequence length is longer than the specified maximum sequence length for this model (1830 > 1024). Running this sequence through the model will result in indexing errors

 Growing up, the author wrote short stories, programmed on an IBM 1401, and nagged his father to buy
him a TRS-80 microcomputer. He wrote simple games, a program to predict how high his model rockets
would fly, and a word processor. He also studied philosophy in college, but switched to AI after
becoming bored with it. He then took art classes at Harvard and applied to art schools, eventually
attending RISD.

response = query_engine.query("What was a hard moment for the author?")
print(textwrap.fill(str(response), 100))

 A hard moment for the author was when he realized that the AI programs of the time were a hoax and
that there was an unbridgeable gap between what they could do and actually understanding natural
language. He had invested a lot of time and energy into learning about AI and was disappointed to
find out that it was not going to get him the results he had hoped for.

带过滤器的查询

from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text="The Shawshank Redemption",
        metadata={
            "author": "Stephen King",
            "theme": "Friendship",
        },
    ),
    TextNode(
        text="The Godfather",
        metadata={
            "director": "Francis Ford Coppola",
            "theme": "Mafia",
        },
    ),
    TextNode(
        text="Inception",
        metadata={
            "director": "Christopher Nolan",
        },
    ),
]

from llama_index.core import StorageContext

vector_store = DocArrayInMemoryVectorStore()
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = GPTVectorStoreIndex(nodes, storage_context=storage_context)

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="theme", value="Mafia")]
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("What is inception about?")

[NodeWithScore(node=Node(text='director: Francis Ford Coppola\ntheme: Mafia\n\nThe Godfather', doc_id='41c99963-b200-4ce6-a9c4-d06ffeabdbc5', embedding=None, doc_hash='b770e43e6a94854a22dc01421d3d9ef6a94931c2b8dbbadf4fdb6eb6fbe41010', extra_info=None, node_info=None, relationships={<DocumentRelationship.SOURCE: '1'>: 'None'}), score=0.7681788983417586)]